U.S. patent number 11,201,801 [Application Number 16/662,577] was granted by the patent office on 2021-12-14 for machine learning-based determinations of lifespan information for devices in an internet of things environment.
This patent grant is currently assigned to Dell Products L.P.. The grantee listed for this patent is Dell Products L.P.. Invention is credited to Hung T. Dinh, Bijan K. Mohanty, Vinod V. Nair.
United States Patent |
11,201,801 |
Dinh , et al. |
December 14, 2021 |
Machine learning-based determinations of lifespan information for
devices in an internet of things environment
Abstract
Methods, apparatus, and processor-readable storage media for
machine learning-based determinations of lifespan information for
devices in an Internet of Things (IoT) environment are provided
herein. An example computer-implemented method includes
automatically obtaining device telemetry data from one or more
IoT-enabled devices within an IoT network, automatically
determining lifespan-related information pertaining to at least a
portion of the one or more IoT-enabled devices by applying a
machine learning model to the device telemetry data, and initiating
at least one automated action in response to the determined
lifespan-related information.
Inventors: |
Dinh; Hung T. (Austin, TX),
Mohanty; Bijan K. (Austin, TX), Nair; Vinod V. (Cedar
Park, TX) |
Applicant: |
Name |
City |
State |
Country |
Type |
Dell Products L.P. |
Round Rock |
TX |
US |
|
|
Assignee: |
Dell Products L.P. (Round Rock,
TX)
|
Family
ID: |
1000005993590 |
Appl.
No.: |
16/662,577 |
Filed: |
October 24, 2019 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20210126845 A1 |
Apr 29, 2021 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N
20/00 (20190101); H04L 43/065 (20130101); G06F
17/18 (20130101); H04L 43/04 (20130101) |
Current International
Class: |
H04L
12/26 (20060101); G06F 17/18 (20060101); G06N
20/00 (20190101) |
Field of
Search: |
;709/224 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Andrew T. Conference Room IoT Projectors, Jul. 11, 2018,
https://www.hackster.io/user883426442/conference-room-iot-projectors-e34c-
fe. cited by applicant .
Arslan Ali, et al. IoT Based Smart Projectors, International
Journal of Scientific & Engineering Research vol. 8, Issue 6,
Jun. 2017, ISSN 2229-5518,
https://www.ijser.org/researchpaper/IoT-Based-Smart-Projectors.pdf.
cited by applicant .
PRWeb, Ilumi Solutions and Optoma Demonstrate World's First Io
Projector and Lighting Integration, Sep. 15, 2016,
https://www.prweb.com/releases/2016/09/prweb13689064.htm. cited by
applicant.
|
Primary Examiner: Lazaro; David R
Assistant Examiner: Khurshid; Zia
Attorney, Agent or Firm: Ryan, Mason & Lewis, LLP
Claims
What is claimed is:
1. A computer-implemented method comprising: automatically
obtaining device telemetry data from one or more Internet of
Things-enabled devices within an Internet of Things network,
wherein the device telemetry data pertains to multiple independent
attributes of the one or more Internet of Things-enabled devices;
automatically determining lifespan-related information pertaining
to at least a portion of the one or more Internet of Things-enabled
devices by applying a machine learning model to the device
telemetry data, wherein the determined lifespan-related information
comprises a binary classification between (i) within a
predetermined proximity of end of life and (ii) not within the
predetermined proximity of end of life, and wherein applying the
machine learning model to the device telemetry data comprises:
minimizing a cost function of
.function..theta..theta..times..times..times..theta..function.
##EQU00004## pertaining to calculating end of life information for
at least one of the multiple independent attributes of the one or
more Internet of Things-enabled devices, and generating, for at
least one of the multiple independent attributes of the one or more
Internet of Things-enabled devices, the binary classification by
processing the device telemetry data pertaining to at least a
portion of the multiple independent attributes of the one or more
Internet of Things-enabled devices using at least one sigmoid
function in conjunction with one or more linear regression
techniques comprising .function..theta. ##EQU00005## wherein x
represents the at least one independent attribute,
h(x)=.theta..sup.T x represents the one or more linear regression
techniques, and ##EQU00006## represents the at least one sigmoid
function for calculating a value associated with the binary
classification for the at least one independent attribute; and
initiating at least one automated action in response to the
determined lifespan-related information; wherein the method is
performed by at least one processing device comprising a processor
coupled to a memory.
2. The computer-implemented method of claim 1, wherein the one or
more linear regression techniques comprise one or more single
variate regression techniques.
3. The computer-implemented method of claim 1, wherein the one or
more linear regression techniques comprise one or more
multi-variate regression techniques.
4. The computer-implemented method of claim 1, wherein initiating
the at least one automated action comprises: automatically
generating an incident report pertaining to the at least a portion
of the one or more Internet of Things-enabled devices; and
automatically submitting the incident report to a ticketing
system.
5. The computer-implemented method of claim 1, wherein the device
telemetry data comprise measurements quantifying one or more
variables pertaining to operation of at least a portion of the one
or more Internet of Things-enabled devices.
6. The computer-implemented method of claim 1, further comprising:
generating the machine learning model based at least in part on
device telemetry data collected from multiple devices analogous to
the one or more Internet of Things-enabled devices within the
Internet of Things network.
7. The computer-implemented method of claim 1, wherein the method
is performed in accordance with a predetermined schedule.
8. The computer-implemented method of claim 1, wherein the method
is performed on an ad hoc basis.
9. A non-transitory processor-readable storage medium having stored
therein program code of one or more software programs, wherein the
program code when executed by at least one processing device causes
the at least one processing device: to automatically obtain device
telemetry data from one or more Internet of Things-enabled devices
within an Internet of Things network, wherein the device telemetry
data pertains to multiple independent attributes of the one or more
Internet of Things-enabled devices; to automatically determine
lifespan-related information pertaining to at least a portion of
the one or more Internet of Things-enabled devices by applying a
machine learning model to the device telemetry data, wherein the
determined lifespan-related information comprises a binary
classification between (i) within a predetermined proximity of end
of life and (ii) not within the predetermined proximity of end of
life, and wherein applying the machine learning model to the device
telemetry data comprises: minimizing a cost function of
.function..theta..theta..times..times..times..theta..function.
##EQU00007## pertaining to calculating end of life information for
at least one of the multiple independent attributes of the one or
more Internet of Things-enabled devices, and generating, for at
least one of the multiple independent attributes of the one or more
Internet of Things-enabled devices, the binary classification by
processing the device telemetry data pertaining to at least a
portion of the multiple independent attributes of the one or more
Internet of Things-enabled devices using at least one sigmoid
function in conjunction with one or more linear regression
techniques comprising .function..theta. ##EQU00008## wherein x
represents the at least one independent attribute,
h(x)=.theta..sup.T x represents the one or more linear regression
techniques, and ##EQU00009## represents the at least one sigmoid
function for calculating a value associated with the binary
classification for the at least one independent attribute; and to
initiate at least one automated action in response to the
determined lifespan-related information.
10. The non-transitory processor-readable storage medium of claim
9, wherein the one or more linear regression techniques comprise
one or more single variate regression techniques.
11. The non-transitory processor-readable storage medium of claim
9, wherein the one or more linear regression techniques comprise
one or more multi-variate regression techniques.
12. The non-transitory processor-readable storage medium of claim
9, wherein initiating the at least one automated action comprises:
automatically generating an incident report pertaining to the at
least a portion of the one or more Internet of Things-enabled
devices; and automatically submitting the incident report to a
ticketing system.
13. The non-transitory processor-readable storage medium of claim
9, wherein the program code when executed by the at least one
processing device causes the at least one processing device: to
generate the machine learning model based at least in part on
device telemetry data collected from multiple devices analogous to
the one or more Internet of Things-enabled devices within the
Internet of Things network.
14. The non-transitory processor-readable storage medium of claim
9, wherein the device telemetry data comprise measurements
quantifying one or more variables pertaining to operation of at
least a portion of the one or more Internet of Things-enabled
devices.
15. An apparatus comprising: at least one processing device
comprising a processor coupled to a memory; the at least one
processing device being configured: to automatically obtain device
telemetry data from one or more Internet of Things-enabled devices
within an Internet of Things network, wherein the device telemetry
data pertains to multiple independent attributes of the one or more
Internet of Things-enabled devices; to automatically determine
lifespan-related information pertaining to at least a portion of
the one or more Internet of Things-enabled devices by applying a
machine learning model to the device telemetry data, wherein the
determined lifespan-related information comprises a binary
classification between (i) within a predetermined proximity of end
of life and (ii) not within the predetermined proximity of end of
life, and wherein applying the machine learning model to the device
telemetry data comprises: minimizing a cost function of
.function..theta..theta..times..times..times..theta..function.
##EQU00010## pertaining to calculating end of life information for
at least one of the multiple independent attributes of the one or
more Internet of Things-enabled devices, and generating, for at
least one of the multiple independent attributes of the one or more
Internet of Things-enabled devices, the binary classification by
processing the device telemetry data pertaining to at least a
portion of the multiple independent attributes of the one or more
Internet of Things-enabled devices using at least one sigmoid
function in conjunction with one or more linear regression
techniques comprising .function..theta. ##EQU00011## wherein x
represents the at least one independent attribute,
h(x)=.theta..sup.T x represents the one or more linear regression
techniques, and ##EQU00012## represents the at least one sigmoid
function for calculating a value associated with the binary
classification for the at least one independent attribute; and to
initiate at least one automated action in response to the
determined lifespan-related information.
16. The apparatus of claim 15, wherein the one or more linear
regression techniques comprise one or more single variate
regression techniques.
17. The apparatus of claim 15, wherein the device telemetry data
comprise measurements quantifying one or more variables pertaining
to operation of at least a portion of the one or more Internet of
Things-enabled devices.
18. The apparatus of claim 15, wherein initiating the at least one
automated action comprises: automatically generating an incident
report pertaining to the at least a portion of the one or more
Internet of Things-enabled devices; and automatically submitting
the incident report to a ticketing system.
19. The apparatus of claim 15, wherein the at least one processing
device is further configured: to generate the machine learning
model based at least in part on device telemetry data collected
from multiple devices analogous to the one or more Internet of
Things-enabled devices within the Internet of Things network.
20. The apparatus of claim 15, wherein the one or more linear
regression techniques comprise one or more multi-variate regression
techniques.
Description
FIELD
The field relates generally to information processing systems, and
more particularly to techniques for providing information
pertaining to devices in such systems.
BACKGROUND
In a multi-user enterprise environment, when a consumable part for
a device reaches its end of life (EOL), that device is typically
unusable until a user logs an incident to a ticketing or
maintenance system to request a repair or a replacement.
Additionally, it is difficult for a user to know or ascertain the
current health status of a device (or a consumable part thereof)
and proactively estimate its end of life. Accordingly, with
conventional device management approaches, such outages and
corresponding actionable remedies are commonly reactive, and
negatively impact user experience as well as productivity.
SUMMARY
Illustrative embodiments of the disclosure provide techniques for
machine learning-based determinations of lifespan information for
devices in an Internet of Things (IoT) environment. An exemplary
computer-implemented method includes automatically obtaining device
telemetry data from one or more IoT-enabled devices within an IoT
network, automatically determining lifespan-related information
pertaining to at least a portion of the one or more IoT-enabled
devices by applying a machine learning model to the device
telemetry data, and initiating at least one automated action in
response to the determined lifespan-related information.
Illustrative embodiments can provide significant advantages
relative to conventional device management techniques. For example,
challenges associated reactive, time-intensive actions are overcome
in one or more embodiments through intelligent and automated
determination of the status of a device or a consumable part
thereof with respect to proximity to its EOL, and automatic
interaction with a maintenance and/or ticketing system to
facilitate one or more automated actions.
These and other illustrative embodiments described herein include,
without limitation, methods, apparatus, systems, and computer
program products comprising processor-readable storage media.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows an information processing system configured for
machine learning-based determinations of lifespan information for
devices in an IoT environment in an illustrative embodiment.
FIG. 2 shows an information processing system configured for
machine learning-based determinations of lifespan information for
devices in an IoT environment in an illustrative embodiment.
FIG. 3 shows example pseudocode for logistics regression in an
illustrative embodiment.
FIG. 4 shows example pseudocode for defining gradient descent and a
cost function in an illustrative embodiment.
FIG. 5 shows example pseudocode for determining an EOL prediction
using logistic regression model in an illustrative embodiment.
FIG. 6 is a flow diagram of a process for machine learning-based
determinations of lifespan information for devices in an IoT
environment in an illustrative embodiment.
FIGS. 7 and 8 show examples of processing platforms that may be
utilized to implement at least a portion of an information
processing system in illustrative embodiments.
DETAILED DESCRIPTION
Illustrative embodiments will be described herein with reference to
exemplary computer networks and associated computers, servers,
network devices or other types of processing devices. It is to be
appreciated, however, that the invention is not restricted to use
with the particular illustrative network and device configurations
shown. Accordingly, the term "computer network" as used herein is
intended to be broadly construed, so as to encompass, for example,
any system comprising multiple networked processing devices.
FIG. 1 shows a computer network (also referred to herein as an
information processing system) 100 configured in accordance with an
illustrative embodiment. The computer network 100 comprises a
plurality of user devices 102-1, 102-2, . . . 102-M, collectively
referred to herein as user devices 102. The computer network 100
also comprises a plurality of IoT clients 103-1, 103-2, . . .
103-N, collectively referred to herein as IoT clients 103. The user
devices 102 and IoT clients 103 are coupled to a network 104, where
the network 104 in this embodiment is assumed to represent a
sub-network or other related portion of the larger computer network
100. Accordingly, elements 100 and 104 are both referred to herein
as examples of "networks" but the latter is assumed to be a
component of the former in the context of the FIG. 1 embodiment.
Also coupled to network 104 is IoT server 105.
The user devices 102 may comprise, for example, mobile telephones,
laptop computers, tablet computers, desktop computers or other
types of computing devices. Such devices are examples of what are
more generally referred to herein as "processing devices." Some of
these processing devices are also generally referred to herein as
"computers." The IoT clients 103 may comprise, for example, laptop
computers, desktop computers, projectors, or other types of devices
having network connectivity. In at least one embodiment, user
devices 102 can be used, for example, to check the status of one or
more of the IoT clients 103.
The user devices 102 and IoT clients 103 in some embodiments
comprise respective devices associated with a particular company,
organization or other enterprise. In addition, at least portions of
the computer network 100 may also be referred to herein as
collectively comprising an "enterprise network." Numerous other
operating scenarios involving a wide variety of different types and
arrangements of processing devices and networks are possible, as
will be appreciated by those skilled in the art.
Also, it is to be appreciated that the term "user" in this context
and elsewhere herein is intended to be broadly construed so as to
encompass, for example, human, hardware, software or firmware
entities, as well as various combinations of such entities.
The network 104 is assumed to comprise a portion of a global
computer network such as the Internet, although other types of
networks can be part of the computer network 100, including a wide
area network (WAN), a local area network (LAN), a satellite
network, a telephone or cable network, a cellular network, a
wireless network such as a Wi-Fi or WiMAX network, or various
portions or combinations of these and other types of networks. The
computer network 100 in some embodiments therefore comprises
combinations of multiple different types of networks, each
comprising processing devices configured to communicate using
internet protocol (IP) or other related communication
protocols.
As also depicted in FIG. 1, enterprise applications 150 are linked
to and associated with the IoT server 105. Such enterprise
applications 150 can include monitoring applications, maintenance
applications, analytics applications, etc. related to one or more
of the user devices 102 and/or IoT clients 103.
Additionally, the IoT server 105 can have an associated database
configured to store data pertaining to user devices and IoT
clients, which comprise, for example, lifecycle and maintenance
information pertaining to various components of such devices and
clients.
Such a database, in at least one embodiment, is implemented using
one or more storage systems associated with the IoT server 105.
Such storage systems can comprise any of a variety of different
types of storage including network-attached storage (NAS), storage
area networks (SANs), direct-attached storage (DAS) and distributed
DAS, as well as combinations of these and other storage types,
including software-defined storage.
Also associated with the IoT server 105, in one or more
embodiments, are input-output devices, which illustratively
comprise keyboards, displays or other types of input-output devices
in any combination. Such input-output devices can be used, for
example, to support one or more user interfaces to the IoT server
105, as well as to support communication between the IoT server 105
and user devices 102, IoT clients 103, and/or other related systems
and devices not explicitly shown.
Additionally, the IoT server 105 in the FIG. 1 embodiment is
assumed to be implemented using at least one processing device.
Each such processing device generally comprises at least one
processor and an associated memory, and implements one or more
functional modules for controlling certain features of the IoT
server 105.
More particularly, the IoT server 105 in such an embodiment can
comprise a processor coupled to a memory and a network
interface.
The processor illustratively comprises a microprocessor, a
microcontroller, an application-specific integrated circuit (ASIC),
a field-programmable gate array (FPGA) or other type of processing
circuitry, as well as portions or combinations of such circuitry
elements.
Such memory illustratively comprises random access memory (RAM),
read-only memory (ROM) or other types of memory, in any
combination. The memory and other memories disclosed herein may be
viewed as examples of what are more generally referred to as
"processor-readable storage media" storing executable computer
program code or other types of software programs.
One or more embodiments include articles of manufacture, such as
computer-readable storage media. Examples of an article of
manufacture include, without limitation, a storage device such as a
storage disk, a storage array or an integrated circuit containing
memory, as well as a wide variety of other types of computer
program products. The term "article of manufacture" as used herein
should be understood to exclude transitory, propagating
signals.
Additionally, in at least one embodiment, a network interface
allows the IoT server 105 to communicate over the network 104 with
the user devices 102 and IoT clients 103, and illustratively
comprises one or more conventional transceivers.
The IoT server 105 further comprises an event filtering module 130,
an event correlation module 132, a rules application module 134, an
event logging module 136, a machine learning model 138, a data
mapping module 140, and an application programming interface (API)
integration module 142.
It is to be appreciated that this particular arrangement of modules
130, 132, 134, 136, 138, 140 and 142 illustrated in the IoT server
105 of the FIG. 1 embodiment is presented by way of example only,
and alternative arrangements can be used in other embodiments. For
example, the functionality associated with the modules 130, 132,
134, 136, 138, 140 and 142 in other embodiments can be combined
into a single module, or separated across a larger number of
modules on the IoT server 105 or on one or more of the user devices
102 and/or IoT clients 103. As another example, multiple distinct
processors can be used to implement different ones of the modules
130, 132, 134, 136, 138, 140 and 142 or portions thereof. Also, at
least portions of the modules 130, 132, 134, 136, 138, 140 and 142
may be implemented at least in part in the form of software that is
stored in memory and executed by a processor.
It is to be understood that the particular set of elements shown in
FIG. 1 involving user devices 102 and IoT clients 103 of computer
network 100 is presented by way of illustrative example only, and
in other embodiments additional or alternative elements may be
used. Thus, another embodiment includes additional or alternative
systems, devices and other network entities, as well as different
arrangements of modules and other components.
An exemplary process utilizing modules 130, 132, 134, 136, 138, 140
and 142 of an example IoT server 105 in computer network 100 will
be described in more detail with reference to the flow diagram of
FIG. 6.
FIG. 2 shows an information processing system configured for
machine learning-based determinations of lifespan information for
devices in an IoT environment in an illustrative embodiment. The
information processing system 200 depicted in FIG. 2 is analogous
to the information processing system 100 depicted in FIG. 1, with
similar implementations of user devices 202, network 204,
enterprise applications 250, and an IoT server 205 (including
modules 230, 232, 234, 236, 238, 240 and 242). As distinguished
from FIG. 1, however, FIG. 2 depicts, instead of IoT clients 103,
client devices 207-1, 207-2, . . . 207-P (collectively referred to
herein as client devices 207), and IoT-enabled dongles 209-1,
209-2, . . . 209-D (collectively referred to herein as IoT-enabled
dongles 209).
The IoT-enable dongles 209, also referred to herein as connectivity
devices, provide network connectivity (e.g., via Wi-Fi
capabilities) to the client devices 207 (which do not inherently
possess network connectivity) and enables communication of health
status information from the client devices 207 to the IoT server
205. More generally, as used herein, a dongle (connectivity device)
represents a hardware device that connects to a separate device
(such as a client device 207, for example) to provide the separate
device with functionality not originally within the separate
device's capabilities (such as, for example, network connectivity),
and/or to enable a pass-through to the separate device that adds
functionality not originally within the separate device's
capabilities.
In one or more embodiments, IoT-enabled dongles 209 interface with
client devices 207 using a RS232-C serial interface and/or a
universal serial bus (USB) interface. The RS232-C serial interface
can be utilized to interface (that is, to connect the IoT-enabled
dongle 209) with client devices 207 such as projectors, for
example. The USB interface can be utilized, for example, to
interface (that is, connect the IoT-enabled dongle 209) to a client
device 207 such as a computer, and configure network information
(e.g., Wi-Fi networks) and static information such as location,
client device name, etc. In one or more embodiments, once an
IoT-enabled dongle 209 connects with a network (e.g., a Wi-Fi
network) such as network 204, meta-data such as client device (207)
details, location information, etc. are entered into the
IoT-enabled dongle 209 and sent to one or more servers such as IoT
server 205. Additionally, in at least one embodiment, the
IoT-enabled dongle 209, once connected to network 204, can
automatically update any new firmware, as necessary and/or
available.
Also, in one or more embodiments, the IoT-enabled dongle 209
includes a central processing unit (which can include, for example,
a Wi-Fi controller, an input/output (I/O) interface controller, a
firmware executor, and a device translation engine), which executes
the firmware and polls (i.e., communicates with) a corresponding
client device 207 to obtain health information (pertaining to the
client device 207) using the device translation engine.
Additionally, in such an embodiment, the IoT-enabled dongle 209
then sends at least a portion of such obtained health information
in one or more JSON message format to IoT server 205.
More specifically, in at least one example embodiment, the
IoT-enabled dongle 209 periodically polls a client device 207 such
as a projector using HEX code (in start-of-text (STX) command
end-of-text (ETX) format) for serial interface data related to, for
example, lamp life, brightness, errors, heat and power information.
In such an embodiment, the projector provides American Standard
Code for Information Interchange (ASCII) commands to the
IoT-enabled dongle 209, which are converted (by the IoT-enabled
dongle 209) into HEX code before being provided to the IoT server
205. In one or more embodiments, firmware of the IoT-enabled dongle
209 hosts at least one (updateable) translation table for ASCII
command equivalents for various client device 207 (e.g., projector)
models.
Additionally, in at least one embodiment, the IoT-enabled dongle
209 sends such obtained and/or converted client device 207
attribute metadata to IoT server 205 for use in training one or
more machine learning models, carrying out one or more intelligent
decisions, carrying out one or more proactive and/or predictive
actions, and performing analytics related to the client devices
207. By way of example, API integration module 242 can be utilized
to initiate an automated preemptive and/or remedial action with
respect to one or more client devices 203, in response to an EOL
prediction generated machine learning model 238. Further, in such
an embodiment, the output of the IoT-enabled dongle 209 to the IoT
server 205 can be in a variety of formats.
Accordingly, at least one embodiment of the invention includes
providing an IoT infrastructure (with respect to both client
devices and a server) with one or more machine learning plug-ins
that can proactively predict the end of life of a client device
and/or a consumable part thereof. Such an embodiment includes
acquiring device telemetry data (from the client device) and
applying at least one machine learning model to predict the end of
life information and proactively take action to mitigate outage
risks. Further, one or more embodiments can include operating on a
predetermined schedule or operating on an ad hoc basis.
As also detailed herein, examples of the machine learning models
utilized in accordance with one or more embodiments include one or
more supervised learning models. By way of example, such an
embodiment can include obtaining an input of independent variables
(such as, for a projector client device, lamp hours, ambient
temperature, brightness levels, etc.), applying a linear regression
(summation) to the input to generate an initial output, and
applying a sigmoid function (logistics) to this initial output to
generate a binary classification output (that is, at risk for
failing versus not at risk for failing). Accordingly, in such an
embodiment, a self-tuning, trained algorithm is implemented that
includes a binary logistic regression algorithm that uses a sigmoid
function on the output of the linear regression (single variate or
multi-variate regression) for classification.
By way merely of illustration, consider the use of hypothesis
(h.sub..theta.(x)=.theta..sub.0+.theta..sub.1x) for single variable
or hypothesis
(h.sub..theta.(x)=.theta..sub.0+.theta..sub.1x.sub.1+.theta..sub.2x.sub.2-
+.theta..sub.3x.sub.3+.theta..sub.4x.sub.4) for multiple variable
regression, wherein x.sub.1, x.sub.2, etc. are the independent
attributes (such as, for example, lamp hours used, brightness
level, and ambient temperature). As such, one or more embodiments
can include striving for minimizing the cost function
.function..theta..theta..times..times..times..theta..function.
##EQU00001## to calculate and predict EOL information pertaining to
one or more of the independent variables (such as, for example, how
much lamp life is left in the projector).
By way of further illustration, consider use of the following
example equation of a linear regression: h(x)=.theta..sup.T x.
Additionally, at least one example embodiment can include using a
sigmoid function
.sigma..function. ##EQU00002## to calculate the probability (e.g.,
a value between 0 and 1) of failure of at least one of the
independent variables (such as, for example, a lamp failure in the
projector) and a binary output of YES (1) or NO (0) using a
logistics regression model. Accordingly, in such an example
embodiment, the hypothesis of the logistic regression becomes
.function..theta. ##EQU00003##
Further, one or more embodiments include collecting data from
multiple devices (e.g., IoT clients 103 and/or client devices 203)
within one or more enterprises (on at least one given cloud, for
example) to train the machine learning models and improve
predication accuracy.
As detailed below, FIG. 3, FIG. 4, and FIG. 5 depict example
pseudocode related to one or more aspects of at least one
embodiment. The noted pseudocode utilizes one or more machine
learning libraries to implement a logistics regression model to
train and test one or more device life cycle attributes, and also
leverages a plotting library for generating a scatterplot from
device attributes sample data.
FIG. 3 shows example pseudocode for logistics regression in an
illustrative embodiment. In this embodiment, pseudocode 300 is
executed by or under the control of a computing device, such as IoT
server 105 or 205. For example, the pseudocode 300 may be viewed as
comprising a portion of a software implementation of at least part
of machine learning model 138/238 of the FIG. 1 and FIG. 2
embodiments.
The pseudocode 300 illustrates importing libraries and loading a
projector data set (pd.read_csv(path, header=header)).
Additionally, as detailed in the pseudocode 300, the values (X and
Y axis) are read from the data set and a scatterplot (lamp hours as
the X axis and brightness of lamp as the Y axis) is generated.
It is to be appreciated that this particular pseudocode shows just
one example implementation of a process for logistics regression,
and alternative implementations of the process can be used in other
embodiments.
FIG. 4 shows example pseudocode for defining gradient descent and a
cost function in an illustrative embodiment. In this embodiment,
pseudocode 400 is executed by or under the control of a computing
device, such as IoT server 105 or 205. For example, the pseudocode
400 may be viewed as comprising a portion of a software
implementation of at least part of machine learning model 138/238
of the FIG. 1 and FIG. 2 embodiments.
The pseudocode 400 illustrates an example implementation of a
logistics regression using statistical techniques such as gradient
descent and a sigmoid function for binary classification. The cost
function and probability predictions are implemented as separate
functions that can be used later in one or more additional portions
of code. Also, the pseudocode 400 illustrates building a logistics
regression model using gradient descent and trained with sample
data (passing X, y values) to a fit function. The model, in the
example shown via the pseudocode 400, predicts projector data (with
lamp hour 655 and brightness level 3), and the accuracy of the
model is calculated and printed.
It is to be appreciated that this particular pseudocode shows just
one example implementation of a process for defining gradient
descent and a cost function, and alternative implementations of the
process can be used in other embodiments.
FIG. 5 shows example pseudocode for determining an EOL prediction
using logistic regression model in an illustrative embodiment. In
this embodiment, pseudocode 500 is executed by or under the control
of a computing device, such as IoT server 105 or 205. For example,
the pseudocode 500 may be viewed as comprising a portion of a
software implementation of at least part of machine learning model
138/238 of the FIG. 1 and FIG. 2 embodiments.
The pseudocode 500 illustrates using a library function to build a
logistics regression model. Sample data are used to train the model
and the accuracy of the model is calculated and printed, which can
be compared to one or more alternate approaches of implementing a
model using gradient descent.
It is to be appreciated that this particular pseudocode shows just
one example implementation of a process for determining an EOL
prediction using logistic regression model, and alternative
implementations of the process can be used in other
embodiments.
FIG. 6 is a flow diagram of a process for machine learning-based
determinations of lifespan information for devices in an IoT
environment in an illustrative embodiment. It is to be understood
that this particular process is only an example, and additional or
alternative processes can be carried out in other embodiments.
In this embodiment, the process includes steps 600 through 604.
These steps are assumed to be performed by the IoT server 105
utilizing at least modules 138, 140 and 142.
Step 600 includes automatically obtaining device telemetry data
from one or more IoT-enabled devices within an IoT network. The
device telemetry data can include measurements quantifying one or
more variables pertaining to operation of at least a portion of the
one or more IoT-enabled devices.
Step 602 includes automatically determining lifespan-related
information pertaining to at least a portion of the one or more
IoT-enabled devices by applying a machine learning model to the
device telemetry data. In at least one embodiment, applying the
machine learning model includes implementing one or more linear
regression techniques (such as, for example, one or more single
variate regression techniques and/or one or more multi-variate
regression techniques). Such an embodiment can also include
applying one or more sigmoid functions to at least a portion of an
output of the one or more linear regression techniques.
Step 604 includes initiating at least one automated action in
response to the determined lifespan-related information. In one or
more embodiments, initiating the at least one automated action
includes automatically generating an incident report pertaining to
the at least a portion of the one or more IoT-enabled devices, and
automatically submitting the incident report to a ticketing system.
Also, in at least one embodiment, the determined lifespan-related
information includes a binary classification between (i) within a
predetermined proximity of end of life and (ii) not within the
predetermined proximity of end of life.
Additionally, the techniques depicted in FIG. 6 can also include
generating the machine learning model based at least in part on
device telemetry data collected from multiple devices analogous to
the one or more IoT-enabled devices within the IoT network.
Further, the techniques depicted in FIG. 6 can be performed in
accordance with a predetermined schedule, or can be performed on an
ad hoc basis.
Accordingly, the particular processing operations and other
functionality described in conjunction with the flow diagram of
FIG. 6 are presented by way of illustrative example only, and
should not be construed as limiting the scope of the disclosure in
any way. For example, the ordering of the process steps may be
varied in other embodiments, or certain steps may be performed
concurrently with one another rather than serially.
The above-described illustrative embodiments provide significant
advantages relative to conventional approaches. For example, some
embodiments are configured to provide an intelligent IoT
infrastructure with machine learning plugins to proactively predict
end of life information associated with client devices.
It is to be appreciated that the particular advantages described
above and elsewhere herein are associated with particular
illustrative embodiments and need not be present in other
embodiments. Also, the particular types of information processing
system features and functionality as illustrated in the drawings
and described above are exemplary only, and numerous other
arrangements may be used in other embodiments.
As mentioned previously, at least portions of the information
processing system 100 can be implemented using one or more
processing platforms. A given such processing platform comprises at
least one processing device comprising a processor coupled to a
memory. The processor and memory in some embodiments comprise
respective processor and memory elements of a virtual machine or
container provided using one or more underlying physical machines.
The term "processing device" as used herein is intended to be
broadly construed so as to encompass a wide variety of different
arrangements of physical processors, memories and other device
components as well as virtual instances of such components. For
example, a "processing device" in some embodiments can comprise or
be executed across one or more virtual processors. Processing
devices can therefore be physical or virtual and can be executed
across one or more physical or virtual processors. It should also
be noted that a given virtual device can be mapped to a portion of
a physical one.
Some illustrative embodiments of a processing platform used to
implement at least a portion of an information processing system
comprises cloud infrastructure including virtual machines
implemented using a hypervisor that runs on physical
infrastructure. The cloud infrastructure further comprises sets of
applications running on respective ones of the virtual machines
under the control of the hypervisor. It is also possible to use
multiple hypervisors each providing a set of virtual machines using
at least one underlying physical machine. Different sets of virtual
machines provided by one or more hypervisors may be utilized in
configuring multiple instances of various components of the
system.
These and other types of cloud infrastructure can be used to
provide what is also referred to herein as a multi-tenant
environment. One or more system components, or portions thereof,
are illustratively implemented for use by tenants of such a
multi-tenant environment.
As mentioned previously, cloud infrastructure as disclosed herein
can include cloud-based systems. Virtual machines provided in such
systems can be used to implement at least portions of a computer
system in illustrative embodiments.
In some embodiments, the cloud infrastructure additionally or
alternatively comprises a plurality of containers implemented using
container host devices. For example, as detailed herein, a given
container of cloud infrastructure illustratively comprises a Docker
container or other type of Linux Container (LXC). The containers
are run on virtual machines in a multi-tenant environment, although
other arrangements are possible. The containers are utilized to
implement a variety of different types of functionality within the
system 100. For example, containers can be used to implement
respective processing devices providing compute and/or storage
services of a cloud-based system. Again, containers may be used in
combination with other virtualization infrastructure such as
virtual machines implemented using a hypervisor.
Illustrative embodiments of processing platforms will now be
described in greater detail with reference to FIGS. 7 and 8.
Although described in the context of system 100, these platforms
may also be used to implement at least portions of other
information processing systems in other embodiments.
FIG. 7 shows an example processing platform comprising cloud
infrastructure 700. The cloud infrastructure 700 comprises a
combination of physical and virtual processing resources that are
utilized to implement at least a portion of the information
processing system 100. The cloud infrastructure 700 comprises
multiple virtual machines (VMs) and/or container sets 702-1, 702-2,
. . . 702-L implemented using virtualization infrastructure 704.
The virtualization infrastructure 704 runs on physical
infrastructure 705, and illustratively comprises one or more
hypervisors and/or operating system level virtualization
infrastructure. The operating system level virtualization
infrastructure illustratively comprises kernel control groups of a
Linux operating system or other type of operating system.
The cloud infrastructure 700 further comprises sets of applications
710-1, 710-2, . . . 710-L running on respective ones of the
VMs/container sets 702-1, 702-2, . . . 702-L under the control of
the virtualization infrastructure 704. The VMs/container sets 702
comprise respective VMs, respective sets of one or more containers,
or respective sets of one or more containers running in VMs. In
some implementations of the FIG. 7 embodiment, the VMs/container
sets 702 comprise respective VMs implemented using virtualization
infrastructure 704 that comprises at least one hypervisor.
A hypervisor platform may be used to implement a hypervisor within
the virtualization infrastructure 704, wherein the hypervisor
platform has an associated virtual infrastructure management
system. The underlying physical machines comprise one or more
distributed processing platforms that include one or more storage
systems.
In other implementations of the FIG. 7 embodiment, the
VMs/container sets 702 comprise respective containers implemented
using virtualization infrastructure 704 that provides operating
system level virtualization functionality, such as support for
Docker containers running on bare metal hosts, or Docker containers
running on VMs. The containers are illustratively implemented using
respective kernel control groups of the operating system.
As is apparent from the above, one or more of the processing
modules or other components of system 100 may each run on a
computer, server, storage device or other processing platform
element. A given such element is viewed as an example of what is
more generally referred to herein as a "processing device." The
cloud infrastructure 700 shown in FIG. 7 may represent at least a
portion of one processing platform. Another example of such a
processing platform is processing platform 800 shown in FIG. 8.
The processing platform 800 in this embodiment comprises a portion
of system 100 and includes a plurality of processing devices,
denoted 802-1, 802-2, 802-3, . . . 802-K, which communicate with
one another over a network 804.
The network 804 comprises any type of network, including by way of
example a global computer network such as the Internet, a WAN, a
LAN, a satellite network, a telephone or cable network, a cellular
network, a wireless network such as a Wi-Fi or WiMAX network, or
various portions or combinations of these and other types of
networks.
The processing device 802-1 in the processing platform 800
comprises a processor 810 coupled to a memory 812.
The processor 810 comprises a microprocessor, a microcontroller, an
application-specific integrated circuit (ASIC), a
field-programmable gate array (FPGA) or other type of processing
circuitry, as well as portions or combinations of such circuitry
elements.
The memory 812 comprises random access memory (RAM), read-only
memory (ROM) or other types of memory, in any combination. The
memory 812 and other memories disclosed herein should be viewed as
illustrative examples of what are more generally referred to as
"processor-readable storage media" storing executable program code
of one or more software programs.
Articles of manufacture comprising such processor-readable storage
media are considered illustrative embodiments. A given such article
of manufacture comprises, for example, a storage array, a storage
disk or an integrated circuit containing RAM, ROM or other
electronic memory, or any of a wide variety of other types of
computer program products. The term "article of manufacture" as
used herein should be understood to exclude transitory, propagating
signals. Numerous other types of computer program products
comprising processor-readable storage media can be used.
Also included in the processing device 802-1 is network interface
circuitry 814, which is used to interface the processing device
with the network 804 and other system components, and may comprise
conventional transceivers.
The other processing devices 802 of the processing platform 800 are
assumed to be configured in a manner similar to that shown for
processing device 802-1 in the figure.
Again, the particular processing platform 800 shown in the figure
is presented by way of example only, and system 100 may include
additional or alternative processing platforms, as well as numerous
distinct processing platforms in any combination, with each such
platform comprising one or more computers, servers, storage devices
or other processing devices.
For example, other processing platforms used to implement
illustrative embodiments can comprise different types of
virtualization infrastructure, in place of or in addition to
virtualization infrastructure comprising virtual machines. Such
virtualization infrastructure illustratively includes
container-based virtualization infrastructure configured to provide
Docker containers or other types of LXCs.
As another example, portions of a given processing platform in some
embodiments can comprise converged infrastructure.
It should therefore be understood that in other embodiments
different arrangements of additional or alternative elements may be
used. At least a subset of these elements may be collectively
implemented on a common processing platform, or each such element
may be implemented on a separate processing platform.
Also, numerous other arrangements of computers, servers, storage
products or devices, or other components are possible in the
information processing system 100. Such components can communicate
with other elements of the information processing system 100 over
any type of network or other communication media.
For example, particular types of storage products that can be used
in implementing a given storage system of a distributed processing
system in an illustrative embodiment include all-flash and hybrid
flash storage arrays, scale-out all-flash storage arrays, scale-out
NAS clusters, or other types of storage arrays. Combinations of
multiple ones of these and other storage products can also be used
in implementing a given storage system in an illustrative
embodiment.
It should again be emphasized that the above-described embodiments
are presented for purposes of illustration only. Many variations
and other alternative embodiments may be used. Also, the particular
configurations of system and device elements and associated
processing operations illustratively shown in the drawings can be
varied in other embodiments. Thus, for example, the particular
types of devices, clients, and servers in a given embodiment and
their respective configurations may be varied. Moreover, the
various assumptions made above in the course of describing the
illustrative embodiments should also be viewed as exemplary rather
than as requirements or limitations of the disclosure. Numerous
other alternative embodiments within the scope of the appended
claims will be readily apparent to those skilled in the art.
* * * * *
References